Authors: Jiaming Li; Ying Guo; Josh Wall; Sam West
Addresses: CSIRO Data61, Corner of Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia ' CSIRO Data61, Corner of Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia ' CSIRO Energy Centre, 10 Murray Dwyer Circuit, Mayfield West NSW 2304, Australia ' CSIRO Energy Centre, 10 Murray Dwyer Circuit, Mayfield West NSW 2304, Australia
Abstract: Various faults occurred in the heating, ventilation and airconditioning (HVAC) systems usually lead to more energy consumption and worse thermal comfort inevitably. This paper presents a feasible and valid solution of HVAC fault detection and diagnosis (FDD) problem based on statistical machine learning technology. It learns the consistent nature of different types of faults of HVAC operation based on support vector machine (SVM), and then identify types of fault in all subsystems using the statistical relationships between groups of measurements. In order to speed up the learning process, principle component analysis (PCA) has been applied to compress the training data. Our approach models the dynamical sub-systems and sequence data in HVAC system. The learnt models can then be used for automatic fault detection and diagnosis. The approach has been tested on commercial HVAC systems. It had successfully detected and identified a number of typical AHU faults.
Keywords: FDD; fault detection and diagnosis; machine learning; SVM; support vector machine; HVAC system; principle component analysis; PCA.
International Journal of Intelligent Systems Technologies and Applications, 2019 Vol.18 No.1/2, pp.204 - 222
Available online: 02 Feb 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article